Hot metal desulphurisation
Hot metal desulphurisation
The hot metal produced with
blast furnaces contains relatively high amounts of sulphur, which is
detrimental for the mechanical properties of steel. Owing to the
limited desulphurisation capacity of converter processes, it is
commonplace to conduct hot metal desulphurisation in a ladle or a
torpedo car before further treatment at the meltshop. The
desulphurisation reagent is typically injected through a submedged top
Our recent research is related to development of a mathematical
model for hot metal desulphurisation. The objective is to derive a
model, which accounts for the effects of main technological and
operational parameters on the desulphurisation efficiency. The model
calculates the desulphurisation rate based on the basis of
thermodynamic driving force and relevant mass transfer resistances. A
detailed description of the model will be published in the near future.
Alongside the phenomenon-based
approach, data-driven methods (e.g. GA and ANN) have been applied for
predicting hot metal desulphurisation as well as sulphide capacity.
This model employs genetic algorithm for determining the values of the
model parameters based on plant data.
Numerical and physical modelling of fluid flows
Numerical and phyisical modelling of fluid flows aims to establish the
fluid flow field during reagent injection. In this way, it is possible
to gather new information for optimisation of the injection practice,
e.g. reagent size distribution, type of lance and carrier gas flow rate.
In addition mathematical
modelling, high temperature experiments are carried out to investigate
kinetics of metal-slag reactions during and after the hot metal desulphurisation.
mathematical model was developed for hot metal desulphurisation. The
model can be employed for studying effect of the main operating and
technological parameters (e.g.
reagent composition, size distribution and injection rate) on the
efficiency of desulphurisation.
- Data-driven parametrised reaction model, in which the rate
parameters are optimised with an genetic algorithm.
- A genetic algorithm based variable selection method was developed for prediction of hot metal desulphurisation
- T. Vuolio, V.-V. Visuri, T. Paananen, and T. Fabritius,
“Identification of rate, extent and mechanisms of hot metal
resulfurization with CaO–SiO2–NaO2 slag systems,” Metallurgical and
Materials Transactions B, forthcoming.
- V.-V. Visuri, P.
Sulasalmi, T. Vuolio, T. Paananen, T. Haas, H. Pfeifer, and T.
Modelling of the Effect of Reagent Particle Size Distribution on the
Efficiency of Hot Metal Desulphurisation", Proceedings of the 4th
European Steel Technology and Application Days, Steel Institute
Düsseldorf, Germany, 2019, forthcoming.
- T. Vuolio, V.-V. Visuri,
A. Sorsa, T. Paananen, and T. Fabritius, "Genetic Algorithm Based
Variable Selection in Prediction of Hot Metal Desulfurization
Kinetics", Steel Research
S. Tuomikoski, T. Paananen, and T. Fabritius,
"Data-Driven Mathematical Modeling of the Effect of Particle Size
Distribution on the Transitory Reaction Kinetics of Hot Metal
and Materials Transactions B, vol. 49, no. 5, pp.
- P. Pekuri, "Effect of initial slag on the efficiency of hot metal desulphurisation", in progress.
- P. Lehtonen, "Experimental Investigation of Hot Metal
Desulphurisation", Master's thesis, University of Oulu, 2017.
- T. Vuolio, "Improvement potential of primary hot metal
delsulphurization", Master's thesis, University of Oulu, 2017.